Data Visualization with R

RStudio.Cloud Workspace

This is an online version of RStudio where we will be working today. We will use this for the hands-on activity portion. Visit this site and create an account, or log in with a gmail account:

go.ncsu.edu/dvr

Workshop goals

• learn about various packages for making data visualizations in R
• be able to determine which package to use
• understand basic syntax for the most popular package: ggplot2
• gain hands-on practice making visualizations in ggplot2
• learn ways to save visualizations

What is R?

R is a software environment for statistical computing and graphics. Using R you can do rigorous statistical analysis, clean and manipulate data, and create publication-quality graphics.

clustering map

Popularity of R

Stephen Cass, “The 2018 Top Programming Languages”, IEEE Spectrum

R packages

Packages are programs that you import into R to help make tasks easier. The most popular R packages for working with data include dplyr, stringr, tidyr, and ggplot2.

Find a package:

• Google (“Top R packages for ….”)
• Looking at trending R packages on rdocumentation.org

Popular R packages for data viz

• base R
• ggplot2
• highcharter
• leaflet
• plotly
• shiny

Base R Plot

This is an example of a plot created with the base R histogram function.

hist(mtcars$mpg)

Example with R package ggplot2

ggplot(mtcars, aes(x=mpg)) + geom_histogram(binwidth=1, col="white") + theme_classic()

ggplot2

ggplot2 is the most popular visualization package for R. It’s the best all-purpose package for creating many types of 2-dimensional visualizations.

Source: r-statistics.co

highcharter

Highcharter is an R package known as an htmlwidget, which allows you to use popular javascript packages for visualization and create interactive web charts. It’s free to use highcharter unless you are using it for a commercial or government purpose.

data(citytemp)

hc <- highchart() %>% 
hc_xAxis(categories = citytemp$month) %>% 
hc_add_series(name = "Tokyo", data = citytemp$tokyo) %>% 
hc_add_series(name = "London", data = citytemp$london) %>% 
hc_add_series(name = "Other city",
data = (citytemp$tokyo + citytemp$london)/2)

hc

highcharter example

leaflet

Leaflet is popular among web developers for creating interactive web maps. It’s an htmlwidget for R based on LeafletJS.

m <- leaflet(options = leafletOptions(zoomControl = FALSE, dragging=FALSE, minZoom = 15, maxZoom = 15)) %>%
  addTiles() %>%  # Add default OpenStreetMap map tiles
  addMarkers(lng=-78.6697, lat=35.7876,
             popup="Hello World!")
m  # Print the map

leaflet example

plotly

plot_ly(economics, x = ~date, y = ~unemploy / pop)

shiny

shiny is a popular R package for creating web applications.

Deciding on the right package

• static or interactive
• single viz or dashboard
• the nature of the data: geospatial? quantitative? qualitative?
• purpose and licensing

Recommended packages

• 2D vizzes: ggplot2, ggpubr
• 3D vizzes: rgl
• maps: leaflet
• interactive: shiny, plotly, or highcharter
• network graphs: igraph, ggnet, diagrammeR, visNetwork
• web applications: shiny

ggplot2: one of the most popular

ggplot2 was created on the principles of the Layered Grammar of Graphics (2010), by Hadley Wickham and based of off work from Wilkinson, Anand, & Grossman (2005) and Jaques Bertin (1983).

Grammar of Graphics

Credit: RStudio, Inc. • CC BY RStudio • info@rstudio.com • 844-448-1212 • rstudio.com

Tidy Data

Proposed by Hadley Wickham (2014).

Tidy data has the following characteristics: “each variable is a column, each observation is a row, and each type of observational unit is a table.”

credit: Hadley Wickham

Tidy data cont’d

Collapse columns of multiple years into one variable: year

credit: Hadley Wickham

Tidy data cont’d

Unpack columns of more than one variable into distinct columns. Remember: only one column per variable!

credit: Hadley Wickham

The Tidyverse

The tidyverse is a set of packages for working with data that include packages for cleaning, manipulating, and visualizing data in R. We will be using the tidyverse package in our workshop activity. See the tidyverse website For more information about tidyverse package.

Hands-On Activity: Open RStudio.Cloud Workspace

https://go.ncsu.edu/dvr

• Log in with Google, Github, or create an account
  
• We will step through the code together and you will have a chance to practice!